Recommender Systems have been the unsung hero of our time. With the Internet growing at a rapid rate, almost exponentially, without recommender systems most of our online websites and applications would take a long time to respond to a particular search request. Recommender systems were initially created for academics who required recommendations rather than to search through large amounts of data. However, these days every modern day application uses some kind of recommender to give personalised suggestions to its users. For example Amazon gives personalised recommendations in order to increase their sales figures by specifically targeting their customers with the products they are most inclined to buy.

One such recommender is a semantic web recommender. The difference between semantic recommenders and other recommenders is that when recommendations are being made they compare the characteristics in the user's profile with the semantic data i.e. the metadata on the article being recommended rather than the text itself to match the characteristics. The semantic data helps the recommender to search effectively and efficiently an article given a characteristic. This is because semantic data allows the recommender to reason rather than just match the characteristic to the text in the article. We believe that in the future every piece of data on the Internet would have its own semantic meaning and hence we chose to create a semantic-based recommender for such semantically tagged data.

Our recommender, iSquirrel, is a working prototype that was developed, tested and validated as an enterprise application. iSquirrel being a semantic based recommender mainly recommends Wikipedia articles from an existing semantic database called DBpedia. The modular design of our system allowed us to add other kinds of recommendations too, such as YouTube videos and music from Last.fm. These recommendations are based on the profile of the user and we have made the system adaptable by including a feedback module. iSquirrel, as a community based recommender, has the ability to create social networks which are exploited by the system to generate recommendations based on other users' interests.

This report details all the challenges we came across building our system and evaluates the result we obtained.  

